Data Scientist

Job Overview

The primary focus of this role will be to understand the information needs for the business and to provide timely accurate, congruent and reliable information to all stakeholders. Once fully ramped up in the role, the expectation for the incumbent is to also provide insights in a proactive manner for business opportunities that may not be known or easily identifiable by the stakeholders.

Responsibilities and Duties

• Collaborate with both internal and external stakeholders to identify business needs, prioritize objectives, define key performance indicators, and formulate a data-driven strategy.
• Lead data requirement workstreams with business stakeholders and clients by creating a cohesive process where detailed requirements around data exchange between various systems can be captured at the beginning of a new business initiative.
• Demonstrate leadership and initiative in gathering and analyzing information, structuring findings, and delivering presentations to stakeholders at all levels.
• Utilize machine learning algorithms and statistical techniques to extract insights and patterns from large datasets.
• Explore and experiment with new data sources, technologies, and analytical methodologies to enhance predictive modeling capabilities.
• Develop and deploy data-driven solutions that optimize business processes and improve decision-making processes.
• Collaborate with domain experts to translate business problems into analytical solutions and actionable recommendations.
• Conduct exploratory data analysis to identify trends, anomalies, and opportunities for optimization.
• Evaluate and validate model performance, ensuring accuracy, reliability, and scalability.
• Communicate complex analytical findings and technical concepts to non-technical stakeholders in a clear and concise manner.
• Adhere to project timelines to ensure timely achievement of project goals and objectives.

Technical Skills

• Proficiency in Python for data analysis, manipulation, and modeling.
• Strong understanding of statistical concepts and methodologies for hypothesis testing, regression analysis, and predictive modeling.
• Experience with machine learning techniques, including supervised and unsupervised learning algorithms, neural networks, and ensemble methods, using frameworks such as TensorFlow, PyTorch, or scikit-learn for building and deploying machine learning models
• Familiarity with data visualization libraries such as Matplotlib, Seaborn, or ggplot2 for creating informative and visually appealing visualizations.
• Familiarity with Python data science libraries such as Pandas, NumPy, and Scikit-learn for data manipulation, analysis, and modeling.
• Knowledge of database systems and SQL for data retrieval, manipulation, and storage.
• Understanding of big data technologies such as Hadoop, Spark, or Hive for processing and analyzing large volumes of data.
• Proficiency in data preprocessing techniques such as data cleaning, feature engineering, and dimensionality reduction.
• Experience with version control systems such as Git for managing code repositories and collaboration.
• Ability to work with cloud computing platforms (e.g., AWS, Azure, Google Cloud) for data storage, processing, and deployment of machine learning models.

Qualifications and Skills

• Bachelor’s degree (Masters preferred) in a discipline such as Computer Science, Applied Math, Applied Sciences, Engineering, etc.
• Experience in Payments Industry would be an asset but not mandatory
• Proven analytical skills in defining business needs into product requirements
• Proven ability to conduct and/or lead multiple projects with minimal oversight
• Excellent communication and interpersonal skills
• Unquestionable personal and business ethics and integrity